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Data Analysis and Knowledge Discovery
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Question Classification Based on Hierarchical Attention Multi-Channel Convolution Bidirectional GRU
YU Bengong,ZHU Mengdi
(School of Management, Hefei University of Technology)
(Key Laboratory of Process Optimization & Intelligent Decision-making, Ministry of Education, Hefei University of Technology, Hefei 230009, China)
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[Objective] Question classification is the basic task of question answering system. Its accuracy directly affects the overall performance of the system. In view of the short length and sparse features of dialogue questions, multi-level features extraction of the question text can understand the semantics of questions more comprehensively and effectively improve the classification accuracy.

[Methods] In order to enrich the semantic representation of the problem text and fully consider the interrogative words, part of speech and word position features in the question sentences, multi-channel attention feature matrices are constructed based on the multi-feature attention mechanism at the word level. Secondly, the multi-channel feature matrices are convolved to obtain phrase-level feature representation. Then the vector representation is rearranged and fed to bidirectional GRU to access forward and backward semantic features respectively. Finally, the latent topic attention is applied to strengthen the topic information in the bidirectional contextual features, and the bidirectional features are fused to generate the final text vector. Softmax classifier is used to give the sample label.

[Results] The accuracy of proposed model in three Chinese question datasets is 93.89%, 94.47% and 94.23% respectively, with the highest improvement of 5.82% over LSTM and of 4.5% over CNN.

[Limitations] We only use three Chinese question corpus to verify the performance of the model.

[Conclusions] This model can make full use of the semantic features of question texts, effectively overcoming the inaccurate understanding of the intention of questions, and improve the performance of question classification.

Key words Question classification      Multi-channel      Hierarchical attention      Convolution      GRU      
Published: 21 May 2020
ZTFLH:  TP391  

Cite this article:

YU Bengong, ZHU Mengdi. Question Classification Based on Hierarchical Attention Multi-Channel Convolution Bidirectional GRU . Data Analysis and Knowledge Discovery, 0, (): 1-.

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